| The optimization of warehousing layout has always been a hot topic in warehousing research,and it is also an important means to reduce the cost of picking and improve the efficiency of picking.This paper focuses on the optimization of non-traditional storage layout parameterization design,combined with the research experience of domestic and foreign experts and scholars,detailed analysis of the actual Picking operation of Flying-V layout,Fishbone layout,and improved Fishbone layout of all warehouse locations to The possible picking situation of the access point,and according to the picking situation under each layout,the corresponding picking distance model is established.This paper also gives a hybrid optimization algorithm based on traditional genetic algorithm and particle swarm optimization algorithm.This algorithm combines the cross mutation operator of genetic algorithm with the operation of particle swarm algorithm to track and record the group extreme value and individual extreme value,and The use of binary-coded chromosomes improves the speed of each algorithm.In order to compare the effectiveness of each algorithm in solving these three different layout types of mathematical models,a series of experiments were conducted for each storage layout using genetic algorithm,particle swarm optimization algorithm,and hybrid algorithm.The experimental results show that the hybrid algorithm not only has the characteristics of fast convergence and short calculation time of the particle swarm algorithm when processing the Flying-V layout,the Fishbone layout and the improved Fishbone layout,but also has a good optimization ability of the genetic algorithm.Finally,based on the experimental results,the experimental algorithm is combined with the Internet technology to program the non-traditional storage layout parameterized design simulation system to improve the efficiency of the storage layout optimization and user experience. |
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